Abstract
BACKGROUND:
Cardiac amyloidosis (CA) is an underdiagnosed yet treatable cause of heart failure in which timely diagnosis is essential to initiate life-prolonging therapies. While artificial intelligence (AI)-based tools using transthoracic echocardiography (TTE), electrocardiography, or electronic health records have demonstrated promise for CA detection, most rely on single data sources. We aimed to evaluate whether integrating clinical, laboratory, and TTE biomarkers improves the performance of an existing TTE-based AI model for CA detection.
METHODS:
We developed and tested a combined AI echo-clinical model (AI-ECM) incorporating demographics, laboratory biomarkers, and TTE parameters into a previously validated TTE-only AI model (Us2.Ca). Model training and internal validation were performed using the Amyloidosis Imaging International Consortium, a global multiethnic registry comprised of 727 patients with CA and 316 controls, including 202 with suspected transthyretin-CA with negative diagnostic evaluation and 114 patients with biopsy-proven extracardiac light chain amyloidosis without cardiac involvement. Ground truth CA diagnosis was adjudicated per consensus criteria. AI-ECM and Us2.Ca performance was assessed using area under the curve, accuracy, sensitivity, and specificity.